The capability of railway emergency rescue can be enhanced by maintaining the railway emergency rescue network and upgrading its technology. Nowadays, influenced by the factors, such as resource type, personnel distribution, line level, etc., space-time differences may be unavoidable. In the meantime, the general description method of the transportation network may lack the consideration of the rescue transportation particularity, so the strategies of resource allocation, maintenance, and upgrading could be illogical. Hence, in this paper, the gravity model is utilized to improve the classical travel time budget model and to construct the space-time accessibility model, firstly. Then, further exploring the space-time accessibility of nodes and edges of railway emergency rescue network and considering the randomness of travel time, a space-time accessibility measurement method for an emergency network is proposed. Moreover, a global optimization model with accessibility characteristics is then constructed for the maintenance allocation of the emergency rescue transportation network. The results show that the proposed method can solve the maintenance allocation problem of the large-scale rescue network effectively, reduce the risk of maintenance allocation strategy failure caused by unreasonable node index parameters, and provide an effective basis and theoretical support for the rational formulation of railway rescue transportation network maintenance allocation strategy.

The weakness of supervision and management measures in railway emergency rescue led to difficulties of whole of monitoring and disposal on the completion time and effect. In order to solve this problem, the GERTS stochastic network schedule is introduced to the process supervision of railway emergency rescue based on the analysis of process of railway emergency rescue. With critical procedures serving as the nodes and execution process as the edges of the network, the random network model is thus formed. In case of emergency, the simulation of GERTS random network can be used to predict necessary rescue time and disposal efficiency, with previous rescue time and effect for reference. The analysis of the case shows that the simulation obtained by this method is consistent with the practical situation of the rescue. Measure standards from this method can be acquired to evaluate the work schedule and efficiency, efficiently supervising the delay of different divisions during the rescue. The proposed measure improves the efficiency and handling capacity of railway emergency rescue, having certain theoretical guidance significance and application value on railway emergency rescue.

With the proliferation of passenger flow under the condition of network condition, the imbalanced temporal and spatial distribution of passenger flow occurs frequently, which brings enormous challenges to the operation of urban rail systems. Effectively predicting the short-time passenger flow of trains is an important prerequisite to optimize the transportation strategies, respond to the fluctuation of passenger flow and meet the real-time demand. Consequently, the GCN-AM-BiLSTM prediction model is proposed to extract the complex temporal and spatial characteristics of passenger flow. Firstly, the urban rail transit temporal diagram and spatial adjacency matrix are constructed to capture the global spatial characteristics using GCN. Secondly, the attention mechanism is introduced into the BiLSTM to construct the AM-BiLSTM module to extract and assign the importance of temporal characteristics from both the forward and backward dimensions. Finally, the characteristics are integrated based on the fusion network. The performance verification and analysis based on Chengdu Metro in China show that compared with several baseline models, our model achieves the best values in terms of MAE, RMSE and MAPE. The prediction efficiency can fully meet the timeliness requirements of the field, which has good application prospects.

The existing emergency rescue process of high-speed railway mainly depends on the command and experience of emergency managers, and the rescue efficiency fluctuates greatly. In order to solve this problem, this paper constructs a model which can predict the time needed for emergency rescue. Firstly, the emergency rescue process of high-speed railway emergency is analyzed, then the relationship between key rescue tasks and the required time can be obtained according to the data mining of rescue cases and expert consultation, and the GERTS network model is established. Finally, the Yong-Wen line accident rescue case is simulated. The accident of Yong-Wen line was interrupted for 32 hours and 35 minutes, the number of simulations is 10000, and the average time is 32 hours. The results show that the model can accurately predict the time needed for emergency rescue and assist the rescue department to make scientific decisions.

The diversification of transportation organization mode under the condition of network brings great challenges to the operation of urban rail transit system. Effectively predicting the short-term passenger flow state of trains is an important prerequisite to optimize the transportation strategy and cope with the real-time passenger flow fluctuation. Hence a prediction method of short-term passenger flow of urban rail trains based on GCN and BiLSTM is proposed. Firstly, the time sequence diagram is esablished based on the urban rail transit network, the dynamic spatial correlation between the stations is captured by GCN. secondly, the BiLSTM network is constructed to extract the continuity and periodic time change features of train passenger flow from the forward and backward dimensions. Finally, the spatio-temporal features are stitched together based on the feature fusion network. The performance verification and analysis based on Chengdu Metro show that compared with several baseline models, the proposed model has higher detection accuracy, and the prediction efficiency can fully meet the timeliness requirements of the field, which has a good application prospect.